Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

Abstract

Alzheimer's Disease (AD), Parkinson, and other neurodegenerative diseases are a major health problem nowadays. However, in many cases, current therapies are merely palliative and only temporarily slow cognitive decline. In this sense, the discovery of new drugs for the treatment of neurodegenerative diseases is a goal of the major importance. Public databases, like ChEMBL, contain a large amount of data about multiplexing assays of inhibitors of a group of enzymes with special relevance in central nervous system. Mono Amino Oxidases (MAOs), Acetyl Cholinesterase (AChE), Glycogen Synthase Kinase-3 (GSK-3), AChE (AChE), and 5α-reductases (5αRs). This data conform an important information source for the application of multi-target computational models. However, almost all the computational models known focus in only one target. In this work, we developed linear multi-target QSAR models (mt-QSAR) for inhibitors of 8 different enzymes promising in the treatment of different neurodegenerative diseases. In so doing, we combined by the first time the software DRAGON with Moving Average parameters with this objective. The best DRAGON model found predict with very high accuracy, specificity, and sensitivity >90% a very large data set >10000 cases in training and validation series.